Meemansa Sood1, Larissa Lachi-Silva2, Lars Blumenstein3, Daniela Baldoni3, Yu-Yun Ho2
1Novartis Pharma AG, 2Novartis Pharmaceutical Corp., 3Biomedical Research, Novartis
Objectives: Dosimetry is an essential measurement in radioligand therapy (RLT) as it enables dosage and regimen selection to deliver safe and efficacious treatments to patients with a favorable benefit-risk. However, the process of measuring dosimetry is burdensome to patients both in terms of imaging time in a SPECT/CT (Single Photon Emission Computed Tomography/Computed Tomography) scanner and number of visits to the clinical site. Also, for hospitals they result in a significant logistical burden, especially if serial imaging over several days is required. The objective of this work is to demonstrate the application of design optimization and non-linear mixed effects (NLME) modeling to reduce the number of dosimetry imaging timepoints for clinical trials using Phase I dose escalation dosimetry data. This approach is demonstrated using a Phase I clinical trial of [177Lu]Lu-NeoB (NeoRay; NCT03872778). Methods: NLME models were developed in Monolix 2023R1 to describe the kidney and bone marrow time-activity curves (TACs) from 13 patients for the NeoRay clinical trial where 6 dosimetry timepoints (1, 6, 24, 48, 72, 168 h) in Cycle 1 were collected. The estimated model parameters were subsequently fed into PopED [1] for design optimization, to assess the feasibility of reducing the dosimetry collection timepoints for Cycle 1. A sensitivity analysis was conducted to assess the impact of sampling window on the selected designs. In addition, by assuming that subsequent cycles follow the same kinetics as Cycle 1, the TACs were simulated in Simulx 2023R1 using combinations of 2 timepoints and were compared to the full profile in Cycle 1 using the percentage error of area under the curve (%error AUC). The combination of two timepoints that minimized %errorAUC was selected to reduce the dosimetry acquisition to 2 timepoints as the optimal collection timepoints for the subsequent cycles of a Phase I trial or for all cycles of later phase trial with prior experience of dosimetry. As the NeoRay trial only collected dosimetry in Cycle 1, multi-cycle kidney TACs of another RLT with the same radionuclide ([177Lu]Lu-PSMA-617) [2] were used to assess the performance of the proposed methodology. Results: The final kidney and bone marrow models were described by bi-exponential decay functions with random effects on all parameters without covariate effects for dosimetry data (77 observations from 13 patients). All parameters for the final models were estimated with reasonable precision with maximum RSE of 56.6% for kidney and 67.2% at most for bone marrow. The parameter estimates for kidney model, A0 (amplitude of the first exponential term) was estimated to be 0.0084, B0 (amplitude of the second exponential term) was estimated to be 0.012, and k1 and k2, the decay rate constants in bio-distribution and washout phases were estimated at 0.24 and 0.011, respectively. Inter-individual variabilities on these parameters were incorporated and were estimated to be 0.16, 0.29, 0.59 and 0.43 on A0, B0, k1 and k2, respectively. The design optimization led to 2 optimal designs (2, 24, 48,168 h) and (2, 24, 72, 168 h) for Cycle 1 based on the precision of the parameter estimates for both organs. In addition, the sensitivity analysis performed via simulation showed no significant changes in efficiency and precision of parameter estimates. Simulation of TACs using 2 and 72 h provided a median %error AUC of 4.46%, followed by that of 2 h and 168 h with a value of 6.35% and 2 and 48 h with a value of 9.82% for kidney, however, 2 and 168 h had a much higher variability as compared to 2 and 48 h. For bone marrow, 2 and 72 h provided a lower median %error AUC of 12.24% with less variability as compared to 2 and 168h with %error AUC of 11.3%, followed by that of 2 and 48 h. Conclusion: A reduced time point dosimetry approach was developed and evaluated for kidney and bone marrow data from a Phase I dose escalation study of [177Lu]Lu-NeoB (NeoRay). NLME modeling and covariate analysis from multiple timepoint imaging in Cycle 1 can be used for design optimization and reduction of imaging time points from 6 to 4 in the first cycle or 2 in subsequent cycles in Phase I and later phase trials while maintaining reasonable accuracy in determining AUCs of individual TACs, and making collection of dosimetry operationally practical for the clinical sites and less burdensome to patients.
[1] Nyberg J, Ueckert S, Stroemberg EA, Hennig S, Karlsson MO, Hooker AC (2012). “PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool.” Computer Methods and Programs in Biomedicine, 108. [2] Kurth J, Heuschkel M, Tonn A, Schildt A, Hakenberg OW, Krause BJ, Schwarzenböck SM. Streamlined Schemes for Dosimetry of 177Lu-Labeled PSMA Targeting Radioligands in Therapy of Prostate Cancer. Cancers (Basel). 2021 Aug 1;13(15):3884. doi: 10.3390/cancers13153884.
Reference: PAGE 33 (2025) Abstr 11682 [www.page-meeting.org/?abstract=11682]
Poster: Drug/Disease Modelling - Oncology